def drive(nsets,decision):
print options.display()
spamdirs = [get_pathname_option("TestDriver", "spam_directories") % \
i for i in range(1, nsets+1)]
hamdirs = [get_pathname_option("TestDriver", "ham_directories") % \
i for i in range(1, nsets+1)]
spamfns = [(x,y,1) for x in spamdirs for y in os.listdir(x)]
hamfns = [(x,y,0) for x in hamdirs for y in os.listdir(x)]
nham = len(hamfns)
nspam = len(spamfns)
cc = CostCounter.nodelay()
allfns = {}
for fn in spamfns+hamfns:
allfns[fn] = None
d = hammie.open('weaktest.db', False)
hamtrain = 0
spamtrain = 0
n = 0
for dir,name, is_spam in allfns.iterkeys():
n += 1
m=msgs.Msg(dir, name).guts
if debug > 1:
print "trained:%dH+%dS"%(hamtrain,spamtrain)
scr=d.score(m)
if debug > 1:
print "score:%.3f"%scr
if not decision.tooearly():
if is_spam:
if debug > 0:
print "Spam with score %.2f"%scr
cc.spam(scr)
else:
if debug > 0:
print "Ham with score %.2f"%scr
cc.ham(scr)
de = decision(scr,is_spam)
if de == TRAIN_AS_SPAM:
d.train_spam(m)
spamtrain += 1
elif de == TRAIN_AS_HAM:
d.train_ham(m)
hamtrain += 1
if n % 100 == 0:
print "%5d trained:%dH+%dS wrds:%d"%(
n, hamtrain, spamtrain, len(d.bayes.wordinfo))
print cc
print "="*70
print "%5d trained:%dH+%dS wrds:%d"%(
n, hamtrain, spamtrain, len(d.bayes.wordinfo))
print cc
def drive(nsets, decision):
print options.display()
spamdirs = [get_pathname_option("TestDriver", "spam_directories") % i for i in range(1, nsets + 1)]
hamdirs = [get_pathname_option("TestDriver", "ham_directories") % i for i in range(1, nsets + 1)]
spamfns = [(x, y, 1) for x in spamdirs for y in os.listdir(x)]
hamfns = [(x, y, 0) for x in hamdirs for y in os.listdir(x)]
nham = len(hamfns)
nspam = len(spamfns)
cc = CostCounter.nodelay()
allfns = {}
for fn in spamfns + hamfns:
allfns[fn] = None
d = hammie.open("weaktest.db", False)
hamtrain = 0
spamtrain = 0
n = 0
for dir, name, is_spam in allfns.iterkeys():
n += 1
m = msgs.Msg(dir, name).guts
if debug > 1:
print "trained:%dH+%dS" % (hamtrain, spamtrain)
scr = d.score(m)
if debug > 1:
print "score:%.3f" % scr
if not decision.tooearly():
if is_spam:
if debug > 0:
print "Spam with score %.2f" % scr
cc.spam(scr)
else:
if debug > 0:
print "Ham with score %.2f" % scr
cc.ham(scr)
de = decision(scr, is_spam)
if de == TRAIN_AS_SPAM:
d.train_spam(m)
spamtrain += 1
elif de == TRAIN_AS_HAM:
d.train_ham(m)
hamtrain += 1
if n % 100 == 0:
print "%5d trained:%dH+%dS wrds:%d" % (n, hamtrain, spamtrain, len(d.bayes.wordinfo))
print cc
print "=" * 70
print "%5d trained:%dH+%dS wrds:%d" % (n, hamtrain, spamtrain, len(d.bayes.wordinfo))
print cc